Impact of Electrolyte Volume on the Cycling Performance and Impedance Growth of 18650 Li-Ion Cells
Bibliographic record
Abstract
The volume of electrolyte used in commercial cylindrical rechargeable Li-ion batteries needs to be closely controlled to optimize performance. It is widely reported in the open literature that insufficient electrolyte can negatively impact cycling performance. However, there are scant reports showing the impact of excessive amounts of electrolyte on cell performance. Here, we show that adding excessive amounts of electrolyte also negatively impacts the cycling performance, causing cells to show faster capacity fade. The effect can be quite significant, causing a noticeable difference within the first 30 cycles, particularly at higher discharge rates (e.g., 1C to 2C rates, Figure 1). It is important to understand the causes of this “high-volume effect” because it sets an additional constraint on the optimization of the system. We have carried out extensive electrochemical impedance measurements of commercial 3.5 Ah 18650 cylindrical cells with various levels of electrolyte (LiPF6/EC/DMC/EMC; 15/25/56/4 wt%) using PEIS and GEIS protocols at an ambient temperature of 25°C. The collected impedance patterns exhibit typical two loop features reported in the literature 1,2 and were modelled by a series combination of one resistance and two parallel R/C elements to extract the cell ohmic and charge transfer resistance values. The total cell resistance of cells with both nominal and high-volume electrolyte show impedance growth during cycling. However, the high-volume cell shows noticeably higher growth of charge transfer resistance. Details of the experimental work and possible mechanism of this effect will be discussed during the presentation. References N. Ogihara et al., J. Electrochem. Soc., 159, A1034–A1039 (2012). W. Waag, S. Käbitz, and D. U. Sauer, Appl. Energy, 102, 885–897 (2013). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".